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The Industrial Internet of Things (IIoT) necessitates low-latency and ultra-reliable communication to support various applications. Besides, the data generated in IIoT must be geo-tagged to take location-aware timely actions. Therefore, this paper proposes an accurate and energy-efficient localization scheme for localizing the devices in IIoT. We introduce a novel anchor activation protocol for the non-cooperative localization of IIoT devices. The proposed method is sequential, distributed, and error adaptive, that first performs distributed scheduling followed by adaptive selection. This distributed localization scheme is well-suited for IIoT due to the non-cooperative nature of the devices; however, at the same time, it is more challenging because no centralized node can disseminate the anchors’ network information. Therefore, the anchors rely only on the local information from other anchors in their vicinity. The distributed approach can significantly benefit such networks because the centralized node can be a system’s bottleneck. Besides the distributed nature, the decision process of anchor selection for localization considers the transmission energy of the anchors to make it energy efficient. Our results show the effectiveness of the proposed method both in terms of localization accuracy and energy efficiency.

As the number of devices in the Internet of Things (IoT) continues to grow, security threats from suspicious communications have become a critical concern. This paper proposes a quality-of-service (QoS)-aware energy-efficient attack scheme that degrades the performance of suspicious devices through eavesdropping and jamming while preserving the QoS of legitimate devices. The scheme optimizes the attacker’s power to maximize attacker energy efficiency (AEE) under QoS constraints. Additionally, the model is extended to multicast scenarios with multiple legitimate receivers. Analytical and simulation results validate the accuracy of the model, demonstrating the scheme’s effectiveness in achieving high energy efficiency without compromising legitimate QoS.

This study proposes an energy-efficient framework for non-terrestrial networks (NTNs) integrating a low Earth orbit (LEO) satellite, an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS), and a terrestrial user. The framework jointly optimizes the UAV’s 3D trajectory, satellite beamforming vectors, and RIS reflection coefficients to maximize energy efficiency (EE), accounting for UAV propulsion energy consumption and Quality of Service (QoS) constraints. The resulting non-convex fractional problem is solved using a low-complexity iterative algorithm combining successive convex approximation (SCA) and second-order cone programming (SOCP). Simulation results reveal up to 35% EE improvement over baseline schemes, highlighting the framework’s scalability and practicality for sustainable NTN systems.

In this paper, we have investigated the performance of energy efficiency (EE) for Intelligent Transportation Systems (ITS), which recently emerged and advanced to preserve speed as well as safe transportation expansion via a cooperative IRS-relay network. To improve the EE, the relay model has been integrated with an IRS block consisting of a number of passive reflective elements. We analyze the ITS in terms of EE, and achievable rate, with different signal-to-noise ratio (SNR) values under Nakagami-m fading channel conditions that help the system to implement in a practical scenario. From the numerical results it is noticed that the EE for the only relay, IRS, and proposed cooperative relay-IRS-aided network at SNR value of 100 dBm is 30, 17, and 48 bits/joule respectively. In addition, we compare the impact of multi-IRS with the proposed cooperative IRS-relay and conventional relay-supported ITS. Simulation results show that both the proposed cooperative IRS-relay-aided ITS network and multi-IRS-aided network outperform the relay-assisted ITS with the increase in SNR.

Internet of Things (IoT) is an auspicious technology that will connect more number of devices through an internet. The huge number of communication expected to transmit high data securely is an important problem in recent days. In this paper, an Enhanced Energy Efficient Lightweight Cryptography Method which utilizes 8 bit manipulation principle (E3LCM) has been proposed. The proposed method has been verified for speech signal using MATLAB. The hardware complexity has been validated using Sparten3E XC3S500E FPGA devices and it has been found that the proposed method consumes 202mW power and 0.9 Kbytes RAM and it outperforms other methods.

Recently, the simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) has been gaining attention as a key enabler for sixth-generation networks, providing additional links with reduction in power consumption. This paper investigates the STAR-RIS’s potential in a cell-free (CF) massive multiple-input multiple-output (mMIMO) network, where distributed APs serve user over the same time/frequency. We propose a deep deterministic policy gradient framework satisfying system-specific and per-user spectral efficiency constraints, exploiting a post-normalization and a penalized reward. From the simulations, it is revealed the proposed algorithm provides better energy performance than benchmarks, highlighting the benefits of STAR-RIS in the CF network.

Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.

Ultra-wideband (UWB) technology is a good candidate for low-latency high-resolution localization. This paper proposes the -law operation (XLO) to boost the positioning accuracy of UWB localization systems. The proposed scheme significantly reduces the contribution of low-SNR samples and increases the time resolution of the pulse. Thus, the XLO achieves a time-of-arrival (ToA) positioning accuracy that existing methods require more energy and bandwidth to attain. The significant performance gain of the XLO for localization makes the XLO a good candidate for simultaneous positioning and communication with low complexity, high performance, and low power consumption suited for Internet of Things (IoT) devices.

Opportunistic Internet of Things (OppIoT) combines Opportunistic Networks and the Internet of Things to provide opportunistic communication between IoT devices and human-created communities in order to improve network connectivity, reliability, and longevity. This is significant because it introduces a new approach to connect and communicate smart devices in situations where there is no end-to-end connectivity. Due to the non-stable existence of connectivity between nodes, building a scalable, flexible, interoperable, and energy efficient routing protocol is a difficult issue. This study presents a new routing protocol for the Opportunistic Internet of Things called Social relationship based Energy Efficient Routing (SEER), which bases routing decisions on the forwarding feasibility degree, residual energy, and buffer capacity of nodes. SEER surpasses EBR and EDR in terms of delivery probability, hop count, and overhead ratio, according to simulation results utilizing the Opportunistic Network Environment (The ONE) simulator.

This paper proposes a deterministic Global Optimization Algorithm (GOA) for UAV-assisted communications, developed as an enhancement to the benchmark Two-Stage Optimization Algorithm (TSOA). The algorithm simultaneously addresses the dual objectives of maximizing ground user (GU) coverage and minimizing total power consumption in multiple UAV systems. Unlike existing literature, which predominantly relies on heuristic approaches, GOA provides a more precise and systematic solution to achieve optimal performance. Comprehensive simulations demonstrate that GOA achieves a 3.68 % increase in coverage count versus SOA under clustered GU distributions while delivering energy savings approximately 2.47 % (uniform) and 2.6 % (clustered) relative to the TSOA benchmark. Crucially, these efficiency gains are realized while maintaining superior GU coverage maximization versus all benchmarked methods. Both numerical results and visual analyses conclusively validate the proposed algorithm's outperformance of existing benchmarks. ⓒ2025 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NCsingle bondND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

As the cornerstone of IoT-based systems, WSN connecting a wide range of intelligent sensor nodes is expected to bring significant changes in the near future. Due to the limited battery capacity of the sensor node, WSN considers maximizing network lifetime by minimizing the power consumption to be the most important challenge. To this end, we propose a Distributed Adaptive Communication with On/Off switching and Dual queuing for Energy efficiency (DACODE) as a novel asynchronous duty cycling mechanism. The performance evaluation shows that the proposed mechanism significantly reduces power consumption while maintaining network throughput and guaranteeing data urgency and queue stability.

In this paper, a network-independent distributed relay sensor (RS) activating method is proposed for energy-efficient cooperative communications using RSs. To achieve this, we introduce a novel metric termed the signal contribution-to-noise contribution ratio and design the required instantaneous received signal strength at RS for each RS to independently determine the activation. Therefore, the proposed RS activation (i.e., adaptive cooperation) is readily enabled without significant signaling overhead in the networks. The numerical results indicated that the proposed RS activation strategy can improve the received signal-to-noise ratio at a destination sensor with fewer active RSs, extending the lifetime of the sensor network.

Energy and spectral efficiency are attracting attention as “emerging technology” that exploits the opportunities of sensing, communications and artificial intelligence. This paper proposes the features of these state of art techniques a new energy and spectrum efficient technique for 5G and beyond networks. By using Interference aware (IA) beam selection in beam-space MIMO, the large number of users are supported with a smaller number of radio-frequency (RF) chains at the same time and frequency resource. It has been observed from the proposed scheme that the achievable sum-rate of mm-wave channel model is also improved. From the simulation results it is found that Interference aware (IA) beam selection is used in beam-space to minimize the total obligatory RF-chains in mm-wave massive MIMO communication systems without noticeable loss in performance.

Spiking neural networks (SNN), employing eventbased spike computation, can be implemented in hardware where on-chip learning and inference are supported in a powerand area-efficient manner. Although many SNN hardware have been proposed for energy-efficient designs using relatively shallow networks, SNN algorithms that support multi-layer learning need to be implemented in hardware to handle more complex datasets. However, multi-layer learning requires more complicated functions like softmax activation, which makes energy-efficient hardware design difficult. In this paper, we present a zero-spike prediction method to skip the complicated function in the convolution layer. Decomposing the original algorithm, the proposed method skips at least 76.90% of softmax activation operations without classification accuracy degradation.

This study presents model-free reinforcement learn ing methods for economic and ecological adaptive cruise control (Eco-ACC) of connected and autonomous electric vehicles. For model-free optimal control of Eco-ACC, we applied two reinforcement learning methods, Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), in which deep neural networks of actors and critics were trained using IPG CarMaker simulations. For performance demonstrations, the HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front vehicle, and the energy consumptions of the host vehicle and front vehicle were compared. In high-fidelity IPG CarMaker simulations, the proposed reinforcement learning- based Eco-ACC methods demonstrated approximately 3–5% and 10–14% efficiency improvements in highway and city-highway driving scenarios, respectively, when compared with the front vehicle. A video of the CarMaker simulation is available at https://youtu.be/DIXzJxMVig8.

This paper covers what an energy efficient built environment is and its relative importance. A study on energy efficient built environments can be usually divided into four parts, namely, Architecture, Engineering, Community and Economy and Alternative Energy. In an energy efficient built environment one can preserve the earth's resources by efficient utilization of alternative energy. Moreover, sustainability is about people, so this paper is also concerned with the active role man plays in a built environment.

수중무선센서망에서의 센서위치추정은 수중환경의 급격한 신호감쇠 특성으로 인해 기존 지상기반의 무선 주파수 방식이나 GPS를 효과적으로 사용할 수 없다는 구조적 난제를 안고 있다. 음향통신이 가장 현실적인 대안이지만, 수중환경은 심각한 에너지 제약과 더불어 센서배치에 따른 기하학적 특이성 문제를 야기한다. 본 논문은 이러한 문제를 해결하기 위해 ToA 기반의 음향 거리추정 방식을 활용한 UWSN 맞춤형 분산형 센서위치추정 알고리즘을 제안한다. 종종 기하학적 오차요인을 간과하는 기존 분산형 접근법들과 달리, 제안하는 기법은 GDoP 기반의 재선택 메커니즘을 명시적으로 도입하였다. 이는 기하학적 배치가 열악한 기준노드 집합을 능동적으로 배제함으로써 위치추정의 강인성을 보장한다. 제안된 알고리즘은 반복적인 절차를 통해 수행되며, 위치를 성공적으로 파악한 센서노드가 이후 인접 노드들의 새로운 기준노드 역할을 수행함으로써 위치 추정영역을 점진적으로 확장한다. 시뮬레이션 결과는 위치 추정성능이 통신반경과 노드밀도 간의 상호 보완적인 관계에 의해 결정됨을 보여준다. 또한 에너지 효율성 분석 결과, 제안된 분산형 방식은 단거리 음향 전송을 활용함으로써 기존 중앙집중식 방식 대비 전체 네트워크 에너지 소비를 약 30% 절감하는 것으로 나타났다. 결론적으로 본 연구는 자원이 제한된 수중환경에 효과적으로 적용 가능한 강인하고 확장성 높으며 비용 효율적인 센서위치추정 솔루션을 제시한다.

Localization in Underwater Wireless Sensor Networks(UWSNs) faces significant challenges due to signal attenuation affecting standard RF and GPS methods, alongside severe energy and geometric constraints in acoustic environments. To address these challenges, this paper proposes a distributed cooperative localization algorithm tailored for UWSNs using Time of Arrival(ToA) acoustic ranging. A key innovation is the explicit inclusion of a Geometric Dilution of Precision(GDoP)-based re-selection mechanism, which ensures robust positioning by mitigating geometric errors often ignored by traditional methods. Through an iterative process, localized nodes serve as new references, progressively expanding network coverage. Simulation results demonstrate that performance relies on balancing communication range and node density, while energy analysis reveals approximately a 30% reduction in consumption compared to centralized approaches through short-range transmission. Consequently, this study presents a robust, scalable, and cost-effective solution for resource-constrained underwater deployments.

Microwave assisted biodiesel production from crude Pongamia pinnata oil using homogeneous base catalyst (KOH) was unsuccessful because of considerable soap formation. Therefore, a two step process of biodiesel production from high free fatty acid (FFA) oil was investigated. In first step, crude P. pinnata oil was acid catalyzed using H2SO4 and acid value of oil was reduced to less than 4 mg KOH/g. Effect of sulfuric acid concentration, alcohol-oil molar ratio and microwave irradiation time on acid value of oil was studied. Result suggested that 1.5% H2SO4 (w/w), 6：1 methanol oil molar ratio and 3 min microwave irradiation time was sufficient to reduce the acid value of oil from 12 and 22 mg KOH/g to 2.9 and 3.9 mg/KOH/g, respectively. Oil obtained after pretreatment was subsequently used for microwave assisted alkali catalyzed transesterification. A higher biodiesel yield (99.0%) was achieved by adopting two step processes. Microwave energy efficiency during alkali catalyzed transesterification was also investigated. The results suggested a significant energy saving because of reduced reaction time under microwave heating.

Renewable energies use clean sources for energy generation and have the potential to balance the supply and demand of power. One of the best ways to save energy for high-demand time is to preserve it in a battery energy storage system (BESS). Various methods are presented in the last two decades for battery state of charge (SOC) estimation, however, most of them are focused only on a single battery pack and use data without accurate preprocessing and feature selection strategy. Therefore, in this paper, we conduct a comparative analysis of machine learning (ML) models with a specific preprocessing strategy and suggest a high performer model for battery rack SOC estimation. First, we preprocess the data by cleaning, normalizing, selecting important attributes, and then split it into training and testing sets. Next, four ML models are trained using the training data for SOC estimation, and finally, for better evaluation, each model is evaluated on the testing data using various error metrics. After comprehensive experiments, we suggest multilayer perceptron (MLP) due to high performance for batteries rack SOC estimation.

In this paper, I researched about proper classification to increase solar generation efficiency with rooftop PV panels. Solar energy efficiency calculation was limited to the individual’s uses. However, Solar energy’s demands are increasing, we need to city level presumption. In this classification, modeling process was based on Busan. To reduce mistakes, this model’s generation data was made by the average of 2019 data. Python was used for the calculation and modeling process. However, I could not find the area of building from its various type. Hence, I supposed that the area of buildings is maximum in its limitation. Therefore, this model can be different in a real situation. Moreover, this research shows that limitation of building to maximize solar generation quantity can not influence much in solar generation quantity. I think it is because Building Acts provides enough sunshine to houses. Presumptions show that the smaller scale of generation was more profitable than the larger one. Furthermore, estimating solar grids is complex. Therefore, I estimated solar grids with less complexity.